Proactive Digital Therapeutics in Diabetology: Fusing Predictive Analytics, Continuous Glucose Forecasting, and Conversational AI in a Mobile Paradigm

The global prevalence of diabetes necessitates a paradigm shift from reactive treatment to proactive, data-driven chronic disease management. This paper presents the conceptualization and architecture of a novel Android mobile application designed to provide comprehensive diabetes care through predictive analytics and personalized patient engagement. The application integrates a suite of artificial intelligence and machine learning (AI/ML) models to execute three primary clinical tasks: diabetes risk stratification (utilizing Random Forest, XGBoost, and Logistic Regression), continuous blood glucose trend prediction (employing Long Short-Term Memory networks), and dynamic diet recommendation (combining rule-based algorithms with an ML hybrid approach). To bridge the gap between complex algorithmic outputs and patient comprehension, the application features an integrated conversational chatbot that delivers personalized guidance and real-time clinical explanations. By translating high-dimensional physiological data into actionable outpatient metrics, this mobile health solution empowers patients while simultaneously streamlining clinical workflows